Reviewing data from thousands or millions of IoT sensors in real-time is beyond the capability of humans. Smart buildings, energy markets, and factories are all examples where AI is required in the deployment and operation of IoT applications.

The addition of intelligence and processing on small devices at the network edge also raises questions about IoT security.

This track which is hosted by OMG and the Industrial Internet Consortium features state of the practice and state of
technology projects underway by the members of these associations that sit between the intersection of AI and real time IOT.

Said Tabet, Lead Technologist for IoT Strategy and OMG Board Member, DellEMC

1:15 The State of AI and IoT

Said Tabet, Lead Technologist for IoT Strategy and OMG Board Member, DellEMC

1:45 PANEL: Deep Learning for Smart Facilities

Managing and servicing a facility – whether it be a building, a campus, or a seaport – is a complex endeavor with many entities having interest in access to data. It is simply too much for a human or a human-based department to manage and
communicate data about everything occurring in a secure and effective manner. This is where IIoT, intelligent systems, and automation come into play. Our panel of experts discusses how to approach a facility-based deployment.

With billions of connected things deployed around the world, how do manufacturers, service providers, and enterprise end-users create a secure and reliable environment for distributing data? Unlike mobile devices issued to employees, IoT devices have
no local user interface for configuration or management. The need arises for frequent remote software updates and transparent data access on a large scale. This panel explores the need to securely and wirelessly distribute data within an enterprise
IoT deployment.

The industrial manufacturing industry is ripe for automation. Production processes can be optimized by deploying distributed AI and industrial apps strategically at multiple levels of the manufacturing environment. Priority areas to apply AI and analytics
include production quality, cost, and efficiency problems in the manufacturing environment.

Using automation for manufacturing defect identification and resolution

Benefits to the enterprise of implementing predictive maintenance

Identifying the optimal distribution of AI and analytics in a manufacturing setting from the cloud to the edge